Pesticides and non-essential metals in Amazonian aquatic organisms: A Scientometric Overview
Bibliographic record
Abstract
This study aims to assess the scientific knowledge regarding the impact of pesticides and non-essential metals on freshwater aquatic organisms within the Amazon basin. The investigation encompasses a comprehensive analysis, including: i) temporal patterns; ii) methodological approaches; iii) keywords; iv) geographical distribution; v) academic institutions; vi) studied groups of aquatic organisms; and vii) specific environmental contexts investigated. It was used 203 publications in Web of Science and Scopus databases. A discernible ascending trajectory in publication frequency was observed over time, exhibiting a robust and statistically significant correlation with citation counts. The predominant disciplinary focus was discerned to be Environmental Science. Prevalent keywords encapsulated "Mercury," "Fish," "Amazon", "methylmercury" and "bioaccumulation". Noteworthy scholarly contributions emanated primarily from Brazil, with substantive collaboration of the United States, France, Canada and Bolivia. Among the foremost research entities were Brazilian institutions. Bioindicator selection exhibited a distinct predilection for fishes. The diverse spectrum of aquatic environments scrutinized included rivers, lakes, laboratory settings, and reservoirs. This scientometric analysis not only furnishes insights into the global trajectory of research on pesticides and non-essential metals within Amazonian aquatic ecosystems but also identifies prevailing methodologies, research lacunae, and prospects for future investigations within the Amazon basin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.071 | 0.104 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".